Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zhaixin244-wq/fnw --skill knowledge-agentgit clone --depth 1 https://github.com/zhaixin244-wq/fnwWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zhaixin244-wq/fnw/knowledge-agent)<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/knowledge-agent"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/knowledge-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/knowledge-agent"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/knowledge-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.00569 |
| Opus 5 | $0.00023 | $0.00284 |
| Sonnet 5 | $0.00009 | $0.00114 |
| Haiku 4.5 | $0.00005 | $0.00057 |
Grade A, and why
knowledge-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to knowledge-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Agent
Build and query AI-powered knowledge bases from claude-mem observations.
What Are Knowledge Agents?
Knowledge agents are filtered corpora of observations compiled into a conversational AI session. Build a corpus from your observation history, prime it (loads the knowledge into an AI session), then ask it questions conversationally.
Think of them as custom "brains": "everything about hooks", "all decisions from the last month", "all bugfixes for the worker service".
Workflow
Step 1: Build a corpus
build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500
Filter options:
project— filter by project nametypes— comma-separated: decision, bugfix, feature, refactor, discovery, changeconcepts— comma-separated concept tagsfiles— comma-separated file paths (prefix match)query— semantic search querydateStart/dateEnd— ISO date rangelimit— max observations (default 500)
Step 2: Prime the corpus
prime_corpus name="hooks-expertise"
This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora.
Step 3: Query
query_corpus name="hooks-expertise" question="What are the 5 lifecycle hooks and when does each fire?"
The knowledge agent answers from its corpus. Follow-up questions maintain context.
Step 4: List corpora
list_corpora
Shows all corpora with stats and priming status.
Tips
- Focused corpora work best — "hooks architecture" beats "everything ever"
- Prime once, query many times — the session persists across queries
- Reprime for fresh context — if the conversation drifts, reprime to reset
- Rebuild to update — when new observations are added, rebuild then reprime
Maintenance
Rebuild a corpus (refresh with new observations)
rebuild_corpus name="hooks-expertise"
After rebuilding, reprime to load the updated knowledge:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 81 lines · 46 tokens per session scan A 538de006ebe2
knowledge-agent is a skill published in the GitHub repository zhaixin244-wq/fnw (28 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 569 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to knowledge-agent, differing in 0 lines, and is treated as a copy.
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